This article is one of a series of Experiments meant to teach students about how science is done, from generating a hypothesis to designing an experiment to analyzing the results with statistics. You can repeat the steps here and compare your results — or use this as inspiration to design your own experiment.
It’s an exciting moment when you first collect data from an experiment. It’s even more exciting when you tell everyone else what you learned. Scientists often gather at meetings — from student science fairs to adult conferences — to present their findings and to get new ideas. Many times, the presenters initially display their findings on a poster.
So I’m taking my cookie science data and sharing what I learned on a poster. In fact, I’m putting it on two posters. Each will resemble posters you might see at a local science fair. One shows how to present data well. The other deliberately makes some mistakes in presenting those data effectively.
If you make too many mistakes on your poster, it could mean that really cool scientific results will be misunderstood or overlooked.
Keep in mind, a good science poster doesn’t have to be fancy or expensive. Both of my posters had budgets of $10. What makes a good poster stand out is one having what I call the three C’s.
Continuity: The poster should present a continuous story of your experiment. Your poster should tell a story that begins with why you studied the problem that you did and ends with what you conclude from your results. Along the way, it should explain briefly how you tested your hypothesis, what data you gleaned along the way, how confident you are that your data show something meaningful and why the findings may be important to others.
Clarity: When you share your research with others, you want to make sure that what you did is clear. People who read your poster should be able to tell what you did, how you did it and why. They should also be able to see what your results were and what you concluded from them. This means your findings should be easy to read and understand. Poster text should be large. The data should be graphed or charted. And it should be easy to tell apart your hypothesis and conclusions.
Consistency: The style of a poster should be consistent to help the poster look clear. Graphs that measure similar things should look similar. You also may want to stick to just one or two styles of letters, to ease readability.
The bad poster
Below is a picture of my not-so-great poster. Can you spot 10 things that need fixing? I’ve circled them for you on the next slide.
This poster is sloppy and difficult to read. Can you spot 10 mistakes?B. Brookshire/SSP
What’s wrong with this poster?
It looks sloppy. A friend and I put it together in about 20 minutes. The graphs and text aren’t straight and the paper is not cut out and attached neatly.
What hypothesis am I testing? Every good scientific experiment has a hypothesis — an explanation or idea that is being tested. But my poster never explained what my hypothesis was.
“Science” is misspelled. If you want to make a good impression, check your spelling.
Christmas lights are fun, but if you’re project isn’t about Christmas lights, you probably want to leave them off. You want people to look at your results. Not at Christmas lights, glitter or other types of distracting bling.
Other than the Christmas lights and the letters, this poster isn’t very colorful. The graphs aren’t easy to see, and everything seems to blend into the white background.
What do these graphs show? Most aren’t labeled.
The graphs are not consistent. Some are dots, other use bars. Some data are presented sideways and others vertically. Some details are even squished far down to the bottom of the page. This makes it hard to tell what they show and what order to read them in.
Are the differences I saw statistically significant? Only those that are may be worth paying much attention to. But here, the graphs don’t include that information.
The text in the introduction and conclusion sections is very small. Most people aren’t going to come up and lean right in to your poster. Large text will help readers grasp your work quickly.
And whose project was this, anyway? My name isn’t even on it!
Here are 10 mistakes that are easy to spot. But there are many more!B. Brookshire/SSP
Overall, this poster lacks all three C’s. There is no continuity. What order did I do things in? Why did I do them? The poster also has no clarity. The text is small and hard to see, and the graphs lack labels. Finally, there’s no consistency. The graphs are each presented differently, making it hard to tell what I found in my experiments.
How can I do better? I don’t need to spend more money. All I really need to do is spend more time.
The good poster
This poster isn’t perfect. But it is neat and you can easily see what I did in my cookie project. B. Brookshire/SSP
This poster actually cost less than the bad one, but it took about 10 times as long to assemble. All I needed was paper, color printing, some lettering and some construction paper. I cut things out carefully; made sure all edges were straight and generally put together a poster that was neat and easy to read. That takes time.
As you click through the slideshow, you can see that this poster has the three C’s. It’s got continuity. I start at the top left showing why people might need a cookie with no gluten — one of the proteins found in wheat. From there the reader can scan down to see how I tackled the experiments. My hypotheses and findings are in the center, where they can get the most attention. Finally, the bottom right of the poster has my conclusion highlighted in red.
This is my methods section, so readers know how I conducted my experiments.B. Brookshire/SSP
The poster also has clarity. At the top left, I have the goal of my project. Above each set of experiments I have the hypothesis that they were testing. At the bottom right I have a summary of my findings and a conclusion. For each graph, I have included markings to show whether or not a result was statistically significant — meaning unlikely to simply be due to chance. Anyone who reads the poster will be able to tell what I learned in each experiment. The text is large, so people will not have to squint to read it.
Finally, the poster shows consistency. Everything is in the same clean font, or typeface. You’ll want to avoid cluttering the visual clarity with a range of fonts and styles (such as bold or italics) or sizes. The graphs, too, have the same color pattern. This means for each graph you can see which bar represents a control wheat cookie or a gluten-free alternative. The color pattern is explained on the first graph so a reader can easily tell which bar is which. Because the colors stay the same from graph to graph in one experiment, it’s easy to compare the results.
Other good things to include in your poster:
Materials and Methods: I have breakdowns of exactly what materials I used and how I asked people to perform a ranking of my cookies. Now, if people read my poster closely enough, they will be able to repeat my experiment themselves.
Statistics: At the bottom of each graph, there is a small description of what it showed. There is also an explanation of what the asterisks other markings mean so that people can easily grasp important details.
References can be in small type, but it’s still nice to have them if people want to know the scientific papers you read. B. Brookshire/SSPReferences: At the bottom right, below my conclusion, you can see a piece of paper with a smaller font. These are my references, a list of all the scientific papers I read that relate closely to the issues tested in my experiments. Most people will not need to see these. Still, if someone has questions, it’s good to have the list on hand to show how these papers informed my hypothesis, study design or conclusions.
Color! Just because you can’t use lights doesn’t mean a poster needs to be boring. You can use color photos and graph your data in color to catch the attention of people walking by. If your experiment is about cookies, there’s nothing wrong with illustrating a few cookies. And maybe just a little glitter.
And of course, don’t forget to put your name on it!
Have you made a scientific poster? What do you think worked well for you? Please share your posters in the comments.
gluten A pair of proteins — gliadin and glutenin — joined together and found in wheat, rye, spelt and barley. The bound proteins give bread, cake and cookie doughs their elasticity and chewiness. Some people may not be able to comfortably tolerate gluten, however, because they have an allergy to it or suffer from celiac disease.
hypothesis A proposed explanation for a phenomenon. In science, a hypothesis is an idea that must be rigorously tested before it is accepted or rejected.
p value (in research and statistics) This is the probability of seeing a difference as big or bigger than the one observed if there is no effect of the variable being tested. Scientists generally conclude that a p value of less than five percent (written 0.05) is statistically significant, or unlikely to occur due to some factor other than the one tested.
standard error of the mean (in statistics) The likely distribution of numbers in a data set, based on a random sample.
statistical analysis A mathematical process that allows scientists to draw conclusions from a set of data. In research, a result is significant (from a statistical point of view) if the observed difference between two or more conditions is unlikely to be due to chance. Obtaining a result that is statistically significant means that it is unlikely to observe that much of a difference if there really is no effect of the conditions being measured.
statistical significance In research, a result is significant (from a statistical point of view) if the likelihood that an observed difference between two or more conditions would not be due to chance. Obtaining a result that is statistically significant means there is a very high likelihood that any difference that is measured was not the result of random accidents.
statistics The practice or science of collecting and analyzing numerical data in large quantities and interpreting their meaning. Much of this work involves reducing errors that might be attributable to random variation. A professional who works in this field is called a statistician.
